Smart resource allocation for mobile edge computing: A deep reinforcement learning approach

J Wang, L Zhao, J Liu, N Kato - IEEE Transactions on emerging …, 2019 - ieeexplore.ieee.org
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …

Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA

T Alfakih, MM Hassan, A Gumaei, C Savaglio… - IEEE …, 2020 - ieeexplore.ieee.org
In recent years, computation offloading has become an effective way to overcome the
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …

iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks

J Chen, S Chen, Q Wang, B Cao… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …

Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things

Y Chen, Z Liu, Y Zhang, Y Wu, X Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Nowadays, driven by the rapid development of smart mobile equipments and 5G network
technologies, the application scenarios of Internet of Things (IoT) technology are becoming …

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Z Tong, X Deng, F Ye, S Basodi, X Xiao, Y Pan - Information Sciences, 2020 - Elsevier
With the popularity of smart mobile equipment, the amount of data requested by users is
growing rapidly. The traditional centralized processing method represented by the cloud …

Learning for computation offloading in mobile edge computing

TQ Dinh, QD La, TQS Quek… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users
(MUs) at the edge of wireless networks. However, deploying MEC systems faces many …

[HTML][HTML] Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

L Huang, X Feng, C Zhang, L Qian, Y Wu - Digital Communications and …, 2019 - Elsevier
The rapid growth of mobile internet services has yielded a variety of computation-intensive
applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which …

Mobile edge computation offloading using game theory and reinforcement learning

S Ranadheera, S Maghsudi, E Hossain - arXiv preprint arXiv:1711.09012, 2017 - arxiv.org
Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile
applications, the computation and storage capabilities of remote cloud has partially migrated …

Task-driven resource assignment in mobile edge computing exploiting evolutionary computation

L Wan, L Sun, X Kong, Y Yuan… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
The IoT network allows IoT devices to communicate with other devices, applications, and
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …

Resource allocation based on deep reinforcement learning in IoT edge computing

X Xiong, K Zheng, L Lei, L Hou - IEEE Journal on Selected …, 2020 - ieeexplore.ieee.org
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …